# -*- coding: utf-8 -*-
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta

from jms.dm.dm_center_inout_base_dt import jms_dm__dm_center_inout_base_dt

jms_dm__dm_center_actualarrival_detail_dt = SparkSqlOperator(
    task_id='jms_dm__dm_center_actualarrival_detail_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    execution_timeout=timedelta(minutes=30),
    email=['shenjiaming@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_center_actualarrival_detail_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_center_actualarrival_detail_dt/execute.sql',
    executor_cores=4,
    executor_memory='2G',
    driver_memory='2G',
    num_executors=4,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启 
        'spark.dynamicAllocation.maxExecutors': 4,  # 动态资源最大扩容 Executor 数
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
        'spark.executor.memoryOverhead': '1G',  # 堆外内存
        'spark.sql.shuffle.partitions': 30,
    },
    hiveconf={
        'hive.exec.dynamic.partition': 'true',  # 动态分区
        'hive.exec.dynamic.partition.mode': 'nonstrict',
        'hive.exec.max.dynamic.partitions': 400,  # 每天生成 20 个分区
        'hive.exec.max.dynamic.partitions.pernode': 400,  # 每天生成 20 个分区
    },
)

jms_dm__dm_center_actualarrival_detail_dt << [
    jms_dm__dm_center_inout_base_dt
]
